Content providers and advertisers hire panelist to yield a representative sample of a desired demographic group of a population of users. Calibrating the representative sample is a technique used to improve estimates and reduce cost of having to construct a larger sample to achieve the same accuracy. Conventional calibration involves finding weights for the representative sample that match the population constraints while attempting to minimize variance (e.g., increasing the precision) of the sample. However, as the number of constraints increases, conventional calibration techniques require more memory than is available on a computer. Therefore, conventional calibration techniques are not scalable for medium-to-large scale problems.
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